SAM for Segment Classification

In the ever-evolving landscape of AI and data annotation, Tictag continuously strives to enhance the annotation process for both efficiency and accuracy.

With our recent integration of Facebook’s "Segment Anything" model (SAM) onto our app, we've been able to enhance the data annotation capabilities of our users - fusing human intelligence and machine learning precision to elevate the accuracy of our datasets, and the speed at which they are created.

Our AI Assisted Tagging feature transforms the process of polygon annotation from a tedious and time-consuming process into a quick and simple task of correcting and refining AI-generated polygons.

Disadvantages of SAM for Segment Classification:

While the Segment Anything model offers incredible capabilities, it's crucial to acknowledge its limitations. Here, we delve into the disadvantages of using SAM for segment classification:

1. Risk of Missing or Misclassified Segments: SAM might not always identify segments accurately, and without proper quality control (QC), this could lead to the loss of valuable data. Rectifying these errors necessitates labor-intensive manual adjustments.

2. Omitted Segments: Even when segments are correctly classified, SAM may omit certain areas. This shortfall demands manual intervention, raising questions about the need for a new model if SAM can fulfill the task alone.

3. Over Reliance on Original Training Data: The performance of any model heavily depends on the quality of its training data. Relying solely on data from another model can undermine the superiority of your own model.

How Prompt-Based AI Assisted Tagging Solves Those Issues:

To harness the potential of SAM and human expertise, we created our AI Assisted Tagging feature. This strategic approach offers a multitude of advantages:

1. Precision Through Focused Annotation: Our approach emphasizes relevant attributes, avoiding unnecessary annotations. For instance, a model designed to count cars won't be distracted by irrelevant details like the sky. This precise targeting enhances accuracy and optimizes costs.

2. Comprehensive Annotations: Seamlessly integrating SAM with human taggers ensures that no segment is missed. With both point-based and bounding box-based prompts, our human taggers ensure thorough annotations, leaving no room for gaps.

3. Fine-Tuned Polygon Refinement: In cases where SAM faces challenges even with prompts, our skilled human taggers manually adjust polygon points. This meticulous refinement guarantees precise and comprehensive annotations.

4. Customized Solutions for Diverse Needs: SAM's versatility is a strong starting point, but acknowledging the uniqueness of tasks is essential. Prompt-based tagging understands that tailored solutions are vital for different annotation requirements.

Tictag's Dedication to Transformative AI-Powered Annotation

Our commitment to advancing AI-powered annotation has led us to introduce AI Assisted Tagging. By synergizing the strengths of SAM with human expertise, we've redefined annotation as a process that not only achieves accuracy but also exceptional efficiency.

Join us in exploring the future, where AI and human intelligence harmoniously converge. At Tictag, we champion the fusion of cutting-edge AI technology with the irreplaceable insights of human taggers, revolutionizing annotation into an intuitive, seamless, and comprehensive process that paves the way for a future where both AI and human intelligence are valued side by side.